| import torch | |
| def compute_contribution_top_feature(features, outputs, weights, labels): | |
| with torch.no_grad(): | |
| total_pre_softmax, predicted_classes = torch.max(outputs, dim=1) | |
| feature_part = features * weights.to(features.device)[predicted_classes] | |
| class_specific_feature_part = torch.zeros((weights.shape[0], features.shape[1],)) | |
| feature_class_part = torch.zeros((weights.shape[0], features.shape[1],)) | |
| for unique_class in predicted_classes.unique(): | |
| mask = predicted_classes == unique_class | |
| class_specific_feature_part[unique_class] = feature_part[mask].mean(dim=0) | |
| gt_mask = labels == unique_class | |
| feature_class_part[unique_class] = feature_part[gt_mask].mean(dim=0) | |
| abs_features = feature_part.abs() | |
| abs_sum = abs_features.sum(dim=1) | |
| fractions_abs = abs_features / abs_sum[:, None] | |
| abs_max = fractions_abs.max(dim=1)[0] | |
| mask = ~torch.isnan(abs_max) | |
| abs_max = abs_max[mask] | |
| return abs_max.mean() |